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Google Professional Data Engineer Exam - Topic 3 Question 71 Discussion

You are developing an application that uses a recommendation engine on Google Cloud. Your solution should display new videos to customers based on past views. Your solution needs to generate labels for the entities in videos that the customer has viewed. Your design must be able to provide very fast filtering suggestions based on data from other customer preferences on several TB of data. What should you do?
C) Build an application that calls the Cloud Video Intelligence API to generate labels. Store data in Cloud Bigtable, and filter the predicted labels to match the user's viewing history to generate preferences.
A) Build and train a complex classification model with Spark MLlib to generate labels and filter the results. Deploy the models using Cloud Dataproc. Call the model from your application.
B) Build and train a classification model with Spark MLlib to generate labels. Build and train a second classification model with Spark MLlib to filter results to match customer preferences. Deploy the models using Cloud Dataproc. Call the models from your application.
D) Build an application that calls the Cloud Video Intelligence API to generate labels. Store data in Cloud SQL, and join and filter the predicted labels to match the user's viewing history to generate preferences.

Google Professional Data Engineer Exam - Topic 3 Question 71 Discussion

Actual exam question for Google's Professional Data Engineer exam
Question #: 71
Topic #: 3
[All Professional Data Engineer Questions]

You are developing an application that uses a recommendation engine on Google Cloud. Your solution should display new videos to customers based on past views. Your solution needs to generate labels for the entities in videos that the customer has viewed. Your design must be able to provide very fast filtering suggestions based on data from other customer preferences on several TB of dat

a. What should you do?

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Suggested Answer: C

by Lucy at Oct 09, 2023, 05:21 AM

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Lettie
8 months ago
Not sure if Cloud Bigtable is the best choice for this use case.
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Tresa
8 months ago
A is solid, but I prefer the simplicity of C.
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Micaela
8 months ago
Surprised that using Cloud SQL is even an option here!
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Noel
9 months ago
I think B is overkill, two models just for filtering?
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Jody
9 months ago
Option C seems the most efficient for quick labeling!
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Avery
9 months ago
I’m a bit confused about whether to use one model or two for filtering. I think having two models could complicate things, but it might be necessary for accuracy.
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Dierdre
9 months ago
I practiced a similar question where we had to choose between different storage options. I feel like Cloud Bigtable is better for handling large datasets like this one.
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Izetta
9 months ago
I'm not entirely sure, but I think using Cloud SQL might slow down the filtering process compared to Cloud Bigtable.
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Detra
10 months ago
I remember we discussed using the Cloud Video Intelligence API in class. It seems like a good fit for generating labels quickly.
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Pura
10 months ago
Whoa, this is a lot to take in. I'm not sure I have the experience to build a full recommendation engine from scratch, especially with all the different Google Cloud services involved. Maybe I'd start by trying to understand the overall architecture and see if I can find any sample code or tutorials to get me started.
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Anna
10 months ago
Okay, I think I've got a plan. I'd start by exploring the Cloud Video Intelligence API and seeing how I can use that to generate the video labels. Then I'd look into Cloud Bigtable as a fast, scalable way to store and filter the data based on customer preferences. I'm feeling pretty confident I can tackle this if I break it down step-by-step.
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Michel
10 months ago
Hmm, this is a tough one. I'm not super familiar with Spark MLlib or Cloud Dataproc, but I think option C might be the way to go. Using the Cloud Video Intelligence API to generate the labels and then storing and filtering the data in Cloud Bigtable sounds like a more straightforward approach.
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Dorian
10 months ago
This looks like a complex problem that requires a lot of different components to be integrated. I'm not sure if I have the skills to build a full solution from scratch, but I think I could at least try to break it down and tackle it piece by piece.
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Johnathon
10 months ago
I'm a bit confused on this one. I know there are a few different objects we could use, but I'm not totally sure which one is the best fit for modeling a client. I'll need to double-check the documentation to make sure I get this right.
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Eliseo
10 months ago
Hmm, I'm a bit confused by this question. It mentions a "series of questions" and that I won't be able to go back and review previous questions. That seems a bit tricky. I'll need to be really careful to understand each question before answering.
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Mel
10 months ago
I think you can't place hosted elements in Elevation Views, but I could be wrong. It's kind of tricky.
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Rima
10 months ago
Okay, let's see. The key here is to determine the appropriate action based on the risk exposure. I think I have a strategy to work through this step-by-step.
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Audra
1 year ago
As a video enthusiast, I'd love to see a recommendation engine that can keep up with my binge-watching habits! Option C sounds like it would be a 'reel' winner.
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Kristel
1 year ago
I think Option C is the way to go for developing an application with a recommendation engine on Google Cloud.
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Taryn
1 year ago
It would definitely help in providing fast filtering suggestions based on past views.
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Fausto
1 year ago
I agree, using the Cloud Video Intelligence API to generate labels and storing data in Cloud Bigtable seems like a solid solution.
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Louvenia
1 year ago
Option C sounds like a great choice for generating labels and filtering suggestions based on customer preferences.
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Stephanie
1 year ago
Hmm, I'm not sure I'd want to build a complex Spark MLlib model just for this task. The Cloud Video Intelligence API in Option C or D looks like a more efficient solution to me.
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Donette
1 year ago
Yeah, I think using the Cloud Video Intelligence API in Option C or D would be the way to go for fast filtering suggestions.
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Salome
1 year ago
I agree, Option D also seems like a good option. Storing data in Cloud SQL could be useful.
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Ocie
1 year ago
Yeah, building and training multiple models in Option B might be overkill. The Cloud Video Intelligence API sounds simpler.
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Quentin
1 year ago
Option C sounds like a good choice. Using the Cloud Video Intelligence API seems efficient.
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Frederic
1 year ago
I agree, Option D also seems like a good option. Storing data in Cloud SQL could be useful.
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Talia
1 year ago
Option C sounds like a good choice. Using the Cloud Video Intelligence API seems efficient.
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Fernanda
1 year ago
Option D seems like a good choice if you're already familiar with Cloud SQL. The simplicity of storing data in a relational database and joining the labels with user history could be a straightforward approach.
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Tonette
1 year ago
That's true, combining the API for labels with Cloud SQL for filtering could be a powerful combination.
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Rashida
1 year ago
But wouldn't using the Cloud Video Intelligence API to generate labels be more accurate?
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Nina
1 year ago
I agree, using Cloud SQL for storing data and joining labels with user history sounds efficient.
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Kenia
1 year ago
Option D seems like a good choice if you're already familiar with Cloud SQL.
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Joana
1 year ago
I'm leaning towards Option B. Having two separate models, one for generating labels and one for filtering, could provide more flexibility and control over the recommendation process.
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Bernardine
1 year ago
I agree, Option B seems like it would give us more control over the recommendation process.
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Frederica
1 year ago
Option B sounds like a good choice. Having separate models for generating labels and filtering could be beneficial.
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Patrick
1 year ago
That's a good point, but I still think option A is more straightforward and easier to implement.
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Lynelle
1 year ago
I disagree, I believe option B is better. Having two classification models for labeling and filtering can provide more accurate results.
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Galen
1 year ago
Option C looks like the most efficient and scalable solution. Using the Cloud Video Intelligence API and Cloud Bigtable seems like a great way to handle the large amounts of data and provide fast filtering capabilities.
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Mary
1 year ago
Yes, using Cloud Bigtable for storing data and filtering predicted labels based on user viewing history is a smart choice for this recommendation engine application.
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Arthur
1 year ago
It's important to have fast filtering suggestions based on customer preferences, and Cloud Bigtable can definitely help with that.
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Jeffrey
1 year ago
I agree, Cloud Video Intelligence API can help generate accurate labels for the videos, and Cloud Bigtable can handle the massive amount of data efficiently.
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Colene
1 year ago
Option C looks like the most efficient and scalable solution. Using the Cloud Video Intelligence API and Cloud Bigtable seems like a great way to handle the large amounts of data and provide fast filtering capabilities.
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Patrick
1 year ago
I think option A is the best choice. Using Spark MLlib for classification and Cloud Dataproc for deployment seems efficient.
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